Abstract
In recent years, content based image retrieval has been concerned because of practical needs on Internet services, especially methods that can improve retrieving speed and precision. Thus, we propose a hashing scheme called Geometry and Topology Preserving Hashing for content based image retrieval. A training process of hashing function involves both of geometric information and topology information is introduced. Compared with state-of-the-art methods, our method gives better precision in experiment on the Oxford Building dataset.
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Acknowledgement
This work was supported in part by the Program 973 under Grant 2012CB316400, in part by the NSFC under Grant 61373113, Grant 61173109, and Grant 61332018, and in part by Microsoft Research Asia.
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Kang, C., Zhu, L., Qian, X. (2016). Scale and Topology Preserving SIFT Feature Hashing. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_19
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DOI: https://doi.org/10.1007/978-3-319-48896-7_19
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